<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>The National Synchrotron Radiation Center SOLARIS, ranked among the top infrastructures of that type worldwide, is the only one located in Central-Eastern Europe, in Poland. The SOLARIS Center, with six fully operational beamlines, serves as a hub for research across a diverse range of disciplines. This cutting-edge facility fosters innovation in fields like biology, chemistry, and physics as well as material engineering, nanotechnology, medicine, and pharmacology. With its advanced infrastructure and multidisciplinary approach, the SOLARIS Center enables discoveries and pushes the boundaries of knowledge. The most important aspect of such enormous research as well as industry infrastructure is to provide stable working conditions for the users and the conducted projects. Due to its unique properties, problem complexities, and challenges that require advanced approaches, the problem of anomaly detection and automatic analysis of signals for the beam stability assessment is still a huge challenge that has not been fully developed. To increase the effectiveness of centers with advanced research infrastructure we focus on the automatic analysis of transverse beam profiles generated by the Pinhole diagnostic beam-line. Pinhole beamlines are typically installed in the middle and high-energy synchrotrons to thoroughly analyze emitted X-rays and therefore assess electron beam quality. To address the problem we take advantage of AI solutions including up-to-date pre-trained convolutional neural network (CNN) models among others EfficientNetB0-B4-B6, InceptionV3 and DenseNet121. In this research, we propose the benchmark for Pinhole image classification including data preprocessing, model implementation, training process, hyperparameter selection as well as testing phase. Created from scratch database contains over one million transverse beam profile images. The proposed solution, based on the InceptionV3 architecture, classifies pinhole beamline images with 94.1% accuracy and 96.6% precision which is a state-of-the-art result in this research area. Finally, we employed interpretability algorithms to perform an analysis of the models and achieved results.</p>
</abstract>

The National Synchrotron Radiation Center SOLARIS, ranked among the top infrastructures of that type worldwide, is the only one located in Central-Eastern Europe, in Poland. The SOLARIS Center, with six fully operational beamlines, serves as a hub for research across a diverse range of disciplines. This cutting-edge facility fosters innovation in fields like biology, chemistry, and physics as well as material engineering, nanotechnology, medicine, and pharmacology. With its advanced infrastructure and multidisciplinary approach, the SOLARIS Center enables discoveries and pushes the boundaries of knowledge. The most important aspect of such enormous research as well as industry infrastructure is to provide stable working conditions for the users and the conducted projects. Due to its unique properties, problem complexities, and challenges that require advanced approaches, the problem of anomaly detection and automatic analysis of signals for the beam stability assessment is still a huge challenge that has not been fully developed. To increase the effectiveness of centers with advanced research infrastructure we focus on the automatic analysis of transverse beam profiles generated by the Pinhole diagnostic beam-line. Pinhole beamlines are typically installed in the middle and high-energy synchrotrons to thoroughly analyze emitted X-rays and therefore assess electron beam quality. To address the problem we take advantage of AI solutions including up-to-date pre-trained convolutional neural network (CNN) models among others EfficientNetB0-B4-B6, InceptionV3 and DenseNet121. In this research, we propose the benchmark for Pinhole image classification including data preprocessing, model implementation, training process, hyperparameter selection as well as testing phase. Created from scratch database contains over one million transverse beam profile images. The proposed solution, based on the InceptionV3 architecture, classifies pinhole beamline images with 94.1% accuracy and 96.6% precision which is a state-of-the-art result in this research area. Finally, we employed interpretability algorithms to perform an analysis of the models and achieved results.

Automatic Analysis and Anomaly Detection System of Transverse Electron Beam Profile Based on Advanced and Interpretable Deep Learning Architectures

Department of Automatic Control and Robotics

AGH University in Krakow, Department of Automatic Control and Robotics

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The National Synchrotron Radiation Center SOLARIS, ranked among the top infrastructures of that type worldwide, is the only one located in Central-Eastern...

Automatic Analysis and Anomaly Detection System of Transverse Electron Beam Profile Based on Advanced and Interpretable Deep Learning Architectures

Online veröffentlicht: 19. März 2024

Seitenbereich: 139 - 156

Eingereicht: 10. Juni 2023

Akzeptiert: 13. Dez. 2023

DOI: https://doi.org/10.2478/jaiscr-2024-0008

Schlüsselwörter
signal analysis, anomaly detection, transfer learning, convolutional neural networks, Synchrotron Radiation System

© 2024 Michał Piekarski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Automatic Analysis and Anomaly Detection System of Transverse Electron Beam Profile Based on Advanced and Interpretable Deep Learning Architectures

Online veröffentlicht: 19. März 2024

Seitenbereich: 139 - 156

Eingereicht: 10. Juni 2023

Akzeptiert: 13. Dez. 2023

DOI: https://doi.org/10.2478/jaiscr-2024-0008

Schlüsselwörtersignal analysis, anomaly detection, transfer learning, convolutional neural networks, Synchrotron Radiation System

© 2024 Michał Piekarski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
signal analysis, anomaly detection, transfer learning, convolutional neural networks, Synchrotron Radiation System